Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval.

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